ML Model Evaluation System with Standardized Class Hierarchies
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Solution Overview
Problem
Evaluating machine learning models is cumbersome and prone to human error when comparing different models trained for distinct but related metrics, as existing methods require replicating experiments and do not effectively reuse components from previous models.
Innovation Solution
A machine learning model evaluation system that defines standardized, extensible class hierarchies for formalizing expected and actual outputs, and computing metrics, allowing for the identification of applicable classes and datasets to automate the evaluation process, thereby standardizing dataset splitting, target creation, and metric computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If experiments are replicated manually for each new machine learning model, then model evaluation can be performed, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The patent creates reusable experiment templates that can be copied and applied to multiple machine learning models. These templates encapsulate the entire evaluation workflow including data preprocessing, model training, and metric computation, allowing experimenters to evaluate new models by simply instantiating the template rather than manually recreating the entire experiment process
Solution Approach 2:
The system implements automated evaluation workflows that execute themselves without manual intervention. The framework automatically retrieves datasets, trains models, computes metrics, and generates reports, eliminating the need for human experimenters to manually perform repetitive tasks and reducing both time consumption and human error
2Adaptability or versatility
If complete new experiments are designed for each model with different metrics, then model-specific evaluation can be achieved, but the complexity of the evaluation process increases
Solution Approach 1:
The patent segments the evaluation process into distinct, modular components: data retrieval, model training, prediction generation, metric computation, and report generation. Each component is implemented as a separate class or function that can be independently configured and reused, allowing the system to adapt to different model requirements while maintaining overall process consistency
Solution Approach 2:
The framework implements a universal experiment template that can evaluate machine learning models across different metrics and datasets. The template uses configurable parameters to adapt to specific model requirements, allowing a single standardized process to serve multiple evaluation scenarios without requiring separate custom experiments for each case
3Ease of operation
If manual experimentation is used for model evaluation, then flexibility in exploring different approaches is maintained, but the process is susceptible to human error
Solution Approach 1:
The system implements automated feedback loops where evaluation results are systematically captured, stored, and used to inform subsequent model iterations. The framework automatically compares model performance against baseline metrics, identifies areas for improvement, and suggests parameter adjustments, providing structured feedback that maintains consistency while preserving flexibility for exploring different approaches
Data Source
AI summary
In one embodiment, a machine learning model evaluation system may define standardized, extensible class hierarchies for evaluating performance of a given machine learning model. The class hierarchies may include a plurality of target classes that formalize an expected output of the given machine learning model based on a given dataset, a plurality of output classes that formalize an actual output of the given machine learning model based on the given dataset, a plurality of metric classes that formalize a comparison of the expected output of the given machine learning model with the actual output of the given machine learning model, and a plurality of datasets. When a machine learning model is received for evaluation, the system may identify a target class, an output class, and a metric class that are applicable to the machine learning model. The system may also retrieve a dataset applicable to the machine learning model.